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Underwater Image Recognition using Machine Learning

Machine Learning is the branch of Artificial Intelligence in which a computer is fed with data and based on that data it tries to find out solution on its own. It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase t...

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Main Authors:	Divya, N.K., Manjula, Sanjay Koti, Priyadarshini, S
Format:	Article
Language:	English English
Published:	INTI International University 2024
Subjects:	QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.intimal.edu.my/2061/1/joit2024_29.pdf http://eprints.intimal.edu.my/2061/2/602 http://eprints.intimal.edu.my/2061/ http://ipublishing.intimal.edu.my/joint.html
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id	my-inti-eprints.2061
record_format	eprints
spelling	my-inti-eprints.20612024-11-27T08:54:20Z http://eprints.intimal.edu.my/2061/ Underwater Image Recognition using Machine Learning Divya, N.K. Manjula, Sanjay Koti Priyadarshini, S QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Machine Learning is the branch of Artificial Intelligence in which a computer is fed with data and based on that data it tries to find out solution on its own. It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase the performance of the algorithms. A Convolutional Neural Network (CNN) is a type of a deep learned an algorithm that has been created for image processing when using convolutional layers to automatically and in a hierarchical way learn features from the input images. Computers can perform well when it comes to image recognition and classification because of its capacity to detect and record such features as edges, or texture, and shapes among others. A rise in focusing on processing underwater images is essential for various research purposes necessary in marine biology, economy as well as in the management of species’ biodiversity. Observance of such organisms as plankton and Posidonia Oceanic allows determining environmental shifts, global warming, and impact of people on sea creatures. These include respectively planktons that are fundamental for oxygen generation, climatic events and the Posidonia Oceanic, which helps improve the sea Biodiversity and water quality. In the organisation study, image processing supplement the physio-chemical analysis and the sonar detection system. The performances of deep learning models, especially the CNNs, in underwater image processing are significantly better than the conventional methodologies. Preprocessing is important because images are often low-quality; data augmentation and transfer learning tackle the problems of a small dataset and class imbalance, which allow you to save computations during training. Through human activities, marine trash remains a menace to deep sea ecosystems and marine organisms calling for proper debris control. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2061/1/joit2024_29.pdf text en cc_by_4 http://eprints.intimal.edu.my/2061/2/602 Divya, N.K. and Manjula, Sanjay Koti and Priyadarshini, S (2024) Underwater Image Recognition using Machine Learning. Journal of Innovation and Technology, 2024 (29). pp. 1-6. ISSN 2805-5179 http://ipublishing.intimal.edu.my/joint.html
institution	INTI International University
building	INTI Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	INTI International University
content_source	INTI Institutional Repository
url_provider	http://eprints.intimal.edu.my
language	English English
topic	QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Divya, N.K. Manjula, Sanjay Koti Priyadarshini, S Underwater Image Recognition using Machine Learning
description	Machine Learning is the branch of Artificial Intelligence in which a computer is fed with data and based on that data it tries to find out solution on its own. It encompasses the procedure for feeding algorithms information to create the algorithms realize patterns in the data and then increase the performance of the algorithms. A Convolutional Neural Network (CNN) is a type of a deep learned an algorithm that has been created for image processing when using convolutional layers to automatically and in a hierarchical way learn features from the input images. Computers can perform well when it comes to image recognition and classification because of its capacity to detect and record such features as edges, or texture, and shapes among others. A rise in focusing on processing underwater images is essential for various research purposes necessary in marine biology, economy as well as in the management of species’ biodiversity. Observance of such organisms as plankton and Posidonia Oceanic allows determining environmental shifts, global warming, and impact of people on sea creatures. These include respectively planktons that are fundamental for oxygen generation, climatic events and the Posidonia Oceanic, which helps improve the sea Biodiversity and water quality. In the organisation study, image processing supplement the physio-chemical analysis and the sonar detection system. The performances of deep learning models, especially the CNNs, in underwater image processing are significantly better than the conventional methodologies. Preprocessing is important because images are often low-quality; data augmentation and transfer learning tackle the problems of a small dataset and class imbalance, which allow you to save computations during training. Through human activities, marine trash remains a menace to deep sea ecosystems and marine organisms calling for proper debris control.
format	Article
author	Divya, N.K. Manjula, Sanjay Koti Priyadarshini, S
author_facet	Divya, N.K. Manjula, Sanjay Koti Priyadarshini, S
author_sort	Divya, N.K.
title	Underwater Image Recognition using Machine Learning
title_short	Underwater Image Recognition using Machine Learning
title_full	Underwater Image Recognition using Machine Learning
title_fullStr	Underwater Image Recognition using Machine Learning
title_full_unstemmed	Underwater Image Recognition using Machine Learning
title_sort	underwater image recognition using machine learning
publisher	INTI International University
publishDate	2024
url	http://eprints.intimal.edu.my/2061/1/joit2024_29.pdf http://eprints.intimal.edu.my/2061/2/602 http://eprints.intimal.edu.my/2061/ http://ipublishing.intimal.edu.my/joint.html
_version_	1817849527972921344
score	13.223943

Underwater Image Recognition using Machine Learning

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